Site Characteristics

Site MAT (C) MAP(mm) Dominant tree species Soil % clay (Mean +/- SD) Soil Fe (ox-extractable)
LENO 18.1 1386 American Sweetgum, American Hornbeam, Possumhaw 44.7 +- 13.8 1.0 +- 0.4
DELA 17.6 1372 Water oak, Red maple, Sugarberry 37.0 +- 6.3 0.88 +- 0.2
ORNL 14.4 1340 Red maple, Sour gum, Chestnut oak 22.0 +- 12.7 0.13 +- 0.05
SERC 13.6 1075 Tulip poplar, American Beech, American Sweetgum 17.4 +- 6.2 0.27 +- 0.1
HARV 7.4 1199 Eastern Hemlock, Northern Red Oak, American Beech 5.5 +- 1.9 0.38 +- 0.1
BART 6.2 1325 American Beech, Eastern Hemlock, Red maple 3.6 +- 0.7 0.76 +- 0.5
TREE 4.8 797 Sugar maple, Red maple, Gray alder 5.4 +- 1.1 0.34 +- 0.09 

## 
## Call:
## lm(formula = proportion.C_HF ~ E, data = final_data %>% filter(Site == 
##     "ORNL"))
## 
## Residuals:
##         1         2         3         4         5         6         7 
## -0.028733  0.071781  0.082462  0.037100 -0.002969 -0.156771 -0.002871 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.84701    0.06261  13.528 3.95e-05 ***
## E           -0.16521    0.11670  -1.416    0.216    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.08803 on 5 degrees of freedom
## Multiple R-squared:  0.2861, Adjusted R-squared:  0.1434 
## F-statistic: 2.004 on 1 and 5 DF,  p-value: 0.216
  proportion MAOM C [MAOM C] proportion MAOM N [MAOM N]
Predictors Estimates std. Error std. Beta standardized std. Error p df Estimates std. Error std. Beta standardized std. Error p df Estimates std. Error std. Beta standardized std. Error p std. p df Estimates std. Error std. Beta standardized std. Error p std. p df
Intercept 0.80 0.04 -0.00 0.15 <0.001 40.00 29.67 7.52 -0.00 0.23 <0.001 40.00 0.88 0.04 0.03 0.12 <0.001 0.824 39.00 1.68 0.43 0.02 0.18 <0.001 0.900 39.00
Mycorrhizal dominance -0.10 0.03 -0.42 0.13 0.003 40.00 -1.66 3.09 -0.06 0.11 0.596 40.00 -0.19 0.07 -0.23 0.12 0.011 0.059 39.00 -1.23 0.56 -0.06 0.11 0.034 0.588 39.00
CDI 0.17 1.24 0.02 0.17 0.893 40.00 -573.04 211.44 -0.67 0.25 0.010 40.00 0.73 1.17 0.50 0.14 0.539 0.001 39.00 -27.98 11.54 -0.24 0.20 0.020 0.224 39.00
FeOx 0.09 0.03 0.49 0.16 0.004 40.00 13.68 3.32 0.61 0.15 <0.001 40.00 0.01 0.02 0.09 0.14 0.521 0.521 39.00 1.00 0.20 0.77 0.15 <0.001 <0.001 39.00
Mycorrhizal dominance *
CDI 		4.34 2.02 0.24 0.11 0.038 0.038 39.00 32.82 15.62 0.21 0.10 0.042 0.042 39.00
Random Effects
σ2 0.00 37.45 0.00 0.13
Ï„00 0.00 Site 27.49 Site 0.00 Site 0.05 Site
ICC 0.09 0.42   0.27
N 7 Site 7 Site 7 Site 7 Site
Observations 46 46 46 46
Marginal R2 / Conditional R2 0.326 / 0.385 0.362 / 0.632 0.422 / NA 0.504 / 0.638

  proportion.C_OLF mg.C.per.g.soil_OLF
Predictors Estimates std. Error std. Beta standardized std. Error p df Estimates std. Error std. Beta standardized std. Error p df
(Intercept) 0.066 0.031 0.004 0.181 0.038 40.000 3.499 1.347 0.004 0.189 0.013 40.000
E 0.047 0.021 0.329 0.148 0.032 40.000 1.282 0.818 0.208 0.133 0.125 40.000
CDI 0.442 0.857 0.105 0.203 0.609 40.000 -78.625 37.834 -0.432 0.208 0.044 40.000
FeOx -0.019 0.020 -0.175 0.182 0.344 40.000 1.203 0.812 0.253 0.171 0.146 40.000
Random Effects
σ2 0.002 2.697
Ï„00 0.000 Site 0.558 Site
ICC 0.099 0.172
N 7 Site 7 Site
Observations 46 46
Marginal R2 / Conditional R2 0.099 / 0.188 0.215 / 0.350
  proportion.N_OLF mg.N.per.g.soil_OLF
Predictors Estimates std. Error std. Beta standardized std. Error p std. p Estimates std. Error std. Beta standardized std. Error p std. p
(Intercept) 0.032 0.026 -0.030 0.133 0.229 0.823 0.100 0.056 -0.006 0.193 0.082 0.975
E 0.108 0.043 0.187 0.139 0.017 0.187 0.068 0.080 0.094 0.136 0.405 0.493
CDI 0.099 0.693 -0.395 0.156 0.887 0.015 -1.997 1.506 -0.484 0.214 0.193 0.029
FeOx 0.006 0.012 0.084 0.161 0.607 0.607 0.049 0.027 0.333 0.185 0.080 0.080
E * CDI -2.587 1.194 -0.281 0.130 0.036 0.036 -1.447 2.245 -0.083 0.129 0.523 0.523
Random Effects
σ2 0.001 0.003
Ï„00 0.000 Site 0.001 Site
ICC   0.168
N 7 Site 7 Site
Observations 46 46
Marginal R2 / Conditional R2 0.247 / NA 0.200 / 0.334
  proportion.C_FLF mg.C.per.g.soil_FLF proportion.N_FLF mg.N.per.g.soil_FLF
Predictors Estimates std. Error std. Beta standardized std. Error p df Estimates std. Error std. Beta standardized std. Error p df Estimates std. Error std. Beta standardized std. Error p df Estimates std. Error std. Beta standardized std. Error p df
(Intercept) 0.136 0.030 0.000 0.136 <0.001 40.000 4.658 1.521 -0.004 0.217 0.004 40.000 0.118 0.021 -0.000 0.124 <0.001 40.000 0.172 0.051 -0.005 0.213 0.002 40.000
E 0.051 0.023 0.284 0.129 0.033 40.000 1.047 0.757 0.168 0.121 0.174 40.000 0.028 0.018 0.200 0.130 0.132 40.000 0.021 0.027 0.098 0.127 0.444 40.000
CDI -0.691 0.833 -0.130 0.157 0.412 40.000 -86.039 42.830 -0.467 0.233 0.051 40.000 -1.597 0.587 -0.394 0.145 0.010 40.000 -3.009 1.448 -0.477 0.230 0.044 40.000
FeOx -0.068 0.021 -0.492 0.151 0.002 40.000 -0.182 0.788 -0.038 0.164 0.819 40.000 -0.025 0.015 -0.235 0.146 0.115 40.000 0.002 0.028 0.013 0.169 0.940 40.000
Random Effects
σ2 0.002 2.266 0.001 0.003
Ï„00 0.000 Site 0.966 Site 0.000 Site 0.001 Site
ICC 0.037 0.299 0.000 0.263
N 7 Site 7 Site 7 Site 7 Site
Observations 46 46 46 46
Marginal R2 / Conditional R2 0.337 / 0.362 0.256 / 0.478 0.323 / 0.323 0.223 / 0.427 
ggarrange(propC.myc.flf, propC.myc.olf, propC.myc, nrow=1, ncol=3, common.legend=T, legend="right", labels=c("a", "b","c"))
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'

ggsave("som.proportion.plots.jpg",path="figures/", width= 30, height= 10, units="cm")

ggarrange(propC.myc.flf, propC.myc.olf, propC.myc, nrow=1, ncol=3, common.legend=T, legend="right", labels=c("a", "b","c"))
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'

ggsave("som.proportion.plots.jpg",path="figures/", width= 30, height= 10, units="cm")
concC.fe.hf=ggplot(final_data, aes(x=FeOx, y=mg.C.per.g.soil_HF, color=Site))+
   geom_smooth(method="lm",size=2, se=F)+
  geom_point()+
   scale_colour_viridis_d()+
  labs(x=" % FeOx ", y="[MAOM C] (mg/g soil)", colour="Site")+
  theme_cowplot()+
  theme(legend.title=element_text(hjust=0.5, size=16), legend.text=element_text(size=15), axis.text=element_text(size=15), axis.title=element_text(size=19))

propC.fe.hf=ggplot(final_data, aes(x=FeOx, y=proportion.C_HF, color=Site))+
   geom_smooth(method="lm",size=2, se=F)+
  geom_point()+
   scale_colour_viridis_d()+
  labs(x=" % FeOx ", y=expression("proportion C"[MAOM]), colour="Site")+
  theme_cowplot()+
  theme(legend.title=element_text(hjust=0.5, size=16), legend.text=element_text(size=15), axis.text=element_text(size=15), axis.title=element_text(size=19))


concN.fe.hf=ggplot(final_data, aes(x=FeOx, y=mg.N.per.g.soil_HF, color=Site))+
   geom_smooth(method="lm",size=2, se=F)+
  geom_point()+
   scale_colour_viridis_d()+
  labs(x=" % FeOx ", y="[MAOM N] (mg/g soil)", colour="Site")+
  theme_cowplot()+
  theme(legend.title=element_text(hjust=0.5, size=16), legend.text=element_text(size=15), axis.text=element_text(size=15), axis.title=element_text(size=19))

ggarrange( propC.fe.hf,concC.fe.hf, nrow=1, ncol=2, common.legend=T, legend="right")
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'

ggsave("hf.conc.prop.fe.pdf",path="figures/", width= 20, height= 10, units="cm")

#what are the slopes of these lines?
fe_conchf_slopes_C=coef(lmList(mg.C.per.g.soil_HF~FeOx|Site , data = final_data))[2]
fe.mod1.slopes= as.data.frame(forests)%>% 
  arrange(forests) %>% 
  cbind(fe_conchf_slopes_C$FeOx) %>% 
  summarize(mean_slope=mean(fe_conchf_slopes_C$FeOx))

fe_conchf_slopes_N=coef(lmList(mg.N.per.g.soil_HF~FeOx|Site , data = final_data))[2]
fe.mod2.slopes= as.data.frame(forests)%>% 
  arrange(forests) %>% 
  cbind(fe_conchf_slopes_N$FeOx) %>% 
  summarize(mean_slope=mean(fe_conchf_slopes_N$FeOx))
  C.N_FLF C.N_OLF C.N_HF
Predictors Estimates std. Error std. Beta standardized std. Error p std. p Estimates std. Error std. Beta standardized std. Error p std. p Estimates std. Error std. Beta standardized std. Error p std. p
(Intercept) 22.642 5.408 -0.002 0.175 <0.001 0.991 20.437 7.337 -0.005 0.203 0.008 0.982 20.415 2.790 -0.016 0.152 <0.001 0.916
E 10.123 8.052 0.410 0.135 0.216 0.004 16.195 10.041 0.494 0.130 0.115 0.001 7.990 3.473 0.167 0.082 0.027 0.049
CDI 93.307 144.967 0.108 0.196 0.524 0.583 192.450 197.621 0.187 0.222 0.336 0.406 -231.952 75.550 -0.785 0.163 0.004 <0.001
FeOx -6.124 2.635 -0.414 0.178 0.025 0.025 -3.573 3.462 -0.188 0.182 0.308 0.308 0.668 1.243 0.064 0.119 0.594 0.594
E * CDI -65.583 224.946 -0.037 0.127 0.772 0.772 -116.203 281.347 -0.051 0.124 0.682 0.682 -166.559 97.547 -0.133 0.078 0.096 0.096
Random Effects
σ2 27.164 41.285 4.859
Ï„00 3.844 Site 11.499 Site 2.249 Site
ICC 0.124 0.218 0.316
N 7 Site 7 Site 7 Site
Observations 46 46 46
Marginal R2 / Conditional R2 0.235 / 0.330 0.219 / 0.389 0.630 / 0.747
  mg.C.per.g.soil_HF mg.N.per.g.soil_HF
Predictors Estimates std. Error std. Beta standardized std. Error p Estimates std. Error std. Beta standardized std. Error p
(Intercept) 15.428 3.499 0.011 0.283 <0.001 1.251 0.188 0.002 0.206 <0.001
plotBA 0.542 1.342 0.055 0.137 0.688 -0.043 0.088 -0.076 0.155 0.628
Random Effects
σ2 52.139 0.255
Ï„00 40.407 Site 0.046 Site
ICC 0.437 0.154
N 7 Site 7 Site
Observations 46 46
Marginal R2 / Conditional R2 0.003 / 0.438 0.005 / 0.159

Correlations.

Fraction modern has no relationship with soil, plant, or climate variables but is inherently different on a site-by-site basis. It also has no bearing on the concentration or proportion of MAOM C in the soil. This is interesting because we hypothesized there would be a connection between the concentration of C in the MAOM fraction and the age of that C (bigger pool, more buidling up= older C and smaller pool= more dynamic pool= younger C) but that does not seem to be the case! :

  d14C
Predictors Estimates std. Error std. Beta standardized std. Error p
(Intercept) 35.42 50.94 -0.03 0.41 0.492
FeOx 19.64 13.84 0.20 0.14 0.165
E -10.03 13.03 -0.08 0.11 0.447
CDI -1505.90 1460.04 -0.43 0.42 0.310
Random Effects
σ2 557.50
Ï„00 Site 1499.97
ICC 0.73
N Site 6
Observations 39
Marginal R2 / Conditional R2 0.082 / 0.751
  d14C d14C
Predictors Estimates std. Error std. Beta standardized std. Error p Estimates std. Error std. Beta standardized std. Error p
(Intercept) -22.76 17.96 -0.03 0.38 0.214 -30.57 50.43 -0.02 0.36 0.548
mg C per g soil HF 0.83 0.54 0.19 0.12 0.134
proportion C HF 27.71 59.64 0.05 0.11 0.645
Random Effects
σ2 539.88 582.81
Ï„00 1308.65 Site 1185.28 Site
ICC 0.71 0.67
N 6 Site 6 Site
Observations 39 39
Marginal R2 / Conditional R2 0.031 / 0.717 0.002 / 0.671
  d14C d14C d14C d14C d14C d14C
Predictors Estimates std. Error std. Beta standardized std. Error p Estimates std. Error std. Beta standardized std. Error p Estimates std. Error std. Beta standardized std. Error p Estimates std. Error std. Beta standardized std. Error p Estimates std. Error std. Beta standardized std. Error p Estimates std. Error std. Beta standardized std. Error p
(Intercept) -106.69 55.31 -0.00 0.46 0.149 -27.47 22.08 -0.00 0.43 0.281 43.26 26.93 -0.00 0.33 0.183 52.16 66.60 -0.00 0.46 0.491 -117.88 64.11 0.00 0.35 0.140 -7.76 32.17 0.00 0.43 0.825
FeOx 56.26 56.00 0.50 0.50 0.389 5.62 29.96 0.14 0.74 0.860 53.61 62.94 0.44 0.51 0.442 -109.38 118.45 -0.50 0.54 0.424 65.38 59.85 0.65 0.60 0.336 76.47 97.59 0.44 0.56 0.490
E -4.06 56.52 -0.04 0.50 0.947 16.95 45.47 0.28 0.74 0.728 -16.47 27.10 -0.31 0.51 0.576 -28.37 46.69 -0.33 0.54 0.586 114.49 68.63 0.99 0.60 0.171 -36.82 29.46 -0.70 0.56 0.300
Observations 6 7 7 6 7 6
R2 / R2 adjusted 0.252 / -0.247 0.157 / -0.265 0.480 / 0.220 0.239 / -0.268 0.418 / 0.127 0.344 / -0.094 

Nugget_Data=final_data %>% 
  select(Site, field_mean_annual_temperature_C, field_mean_annual_precipitation_mm, proportion.C_HF) %>% 
  group_by(Site) %>% 
  summarize(MAT=mean(field_mean_annual_temperature_C),
            MAP=mean(field_mean_annual_precipitation_mm),
            mean_pct.C.MAOM=(mean(proportion.C_HF))*100,
            sd.pct.C.MAOM=sd(proportion.C_HF)*100)
#Figure for each site where the plots are in order of MAOM N concentration and the top three genera in each plot are labels on the dots

ggplot(final_data_tree_spp, aes(x=E, y=mg.N.per.g.soil_HF))+
  geom_point(color="blue")+
  geom_smooth(method="lm", color="blue")+
  geom_label_repel(aes(label=genus))+
  facet_wrap(~Site)+
  theme_cowplot()
## `geom_smooth()` using formula 'y ~ x'
## Warning: ggrepel: 18 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
## Warning: ggrepel: 15 unlabeled data points (too many overlaps). Consider increasing max.overlaps
## ggrepel: 15 unlabeled data points (too many overlaps). Consider increasing max.overlaps
## Warning: ggrepel: 11 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
## Warning: ggrepel: 17 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
## Warning: ggrepel: 9 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
## Warning: ggrepel: 21 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps

ggsave("top_species_MAOM_N_conc.jpg", path="figures/", width= 40, height= 36, units="cm")
## `geom_smooth()` using formula 'y ~ x'
## Warning: ggrepel: 6 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
## Warning: ggrepel: 3 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
## Warning: ggrepel: 1 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
ecm_order=received_samples_plot %>% 
  select(plotID, order) %>% 
  distinct()
## Adding missing grouping variables: `Site`
pctAngio=ggplot(plotPctAngio %>%
                  rename("Angiosperm"="fractionAngiosperm") %>% 
                  mutate(Gymnosperm=1-Angiosperm,
                         plotID=as.character(plotID))%>% 
                           pivot_longer(c(Angiosperm, Gymnosperm), names_to="gymAng", values_to="dominance") %>% 
                  left_join(ecm_order) , aes(x=order, y=dominance, fill=gymAng))+
  geom_bar(position="stack", stat="identity", colour="black")+
  scale_fill_manual( values=c("gray90", "gray20"))+
  xlab("Study Plot")+
  ylab("Proportion of Basal Area")+
  facet_wrap(~Site, scales="free_x")+
  theme_cowplot()+
  theme(axis.text.x = element_blank(), axis.ticks.x = element_blank(),strip.background = element_rect( fill="white"),legend.title=element_blank())
## Joining, by = "plotID"
ggsave("pctAngioGymno.jpg", path="figures/", width= 20, height= 16, units="cm")

BAbyMYC=ggplot(final_data %>% 
                 left_join(ecm_order), aes(x=order, y=plotBA))+
  geom_bar( stat="identity", colour="black")+
  xlab("Study plots in order of increasing ECM dominance")+
  ylab("Basal Area")+
  facet_wrap(~Site, scales="free_x")+
  theme_cowplot()+
  theme(axis.text.x = element_blank(), axis.ticks.x = element_blank(),strip.background = element_rect( fill="white"),legend.title=element_blank())
## Joining, by = c("plotID", "Site")
ggsave("BAmyc.jpg", path="figures/", width= 20, height= 16, units="cm")
#Relationships between site-level climate decomposition index (CDI) and a) soil oxalate-extractable iron content, and b) total tree basal area in our study plots.

basal_area_cdi=ggplot(final_data, aes(x=CDI, y=plotBA))+
   geom_point()+
  stat_summary(fun= mean, fun.min=mean, fun.max=mean, geom="crossbar", width=0.0009, position="dodge")+
  stat_summary(fun.data = mean_se, geom = "errorbar", width=0.0005, position = position_dodge(width = 0.0009))+
                theme_cowplot()+
  xlab("Climate Decomposition Index")+
  ylab("Total Basal Area"
  )


feox_cdi=ggplot(final_data, aes(x=CDI, y=FeOx))+
   geom_point()+
  stat_summary(fun= mean, fun.min=mean, fun.max=mean, geom="crossbar", width=0.0009, position="dodge")+
  stat_summary(fun.data = mean_se, geom = "errorbar", width=0.0005, position = position_dodge(width = 0.0009))+
    geom_smooth(method="lm")+
                theme_cowplot()+
  xlab("Climate Decomposition Index")+
  ylab("Soil Oxalate-Extractable Iron"
  )

ggarrange(basal_area_cdi, feox_cdi, ncol=2, nrow=1, labels= c("a","b"), legend=F)
## `geom_smooth()` using formula 'y ~ x'

ggsave("FigS2.pdf", path="figures/", width= 24, height= 12, units="cm")


summary(lm(FeOx~CDI,data=final_data )) #yes, more FeOx in warmer sites
## 
## Call:
## lm(formula = FeOx ~ CDI, data = final_data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.54369 -0.29261  0.01529  0.14315  1.21466 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)   
## (Intercept)  -0.0886     0.1845  -0.480  0.63335   
## CDI          17.6747     5.1182   3.453  0.00124 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.3719 on 44 degrees of freedom
## Multiple R-squared:  0.2132, Adjusted R-squared:  0.1954 
## F-statistic: 11.93 on 1 and 44 DF,  p-value: 0.001236
summary(lm(mg.C.per.g.soil_HF~FeOx,data=final_data %>% filter(Site=="LENO") )) #yes within this site Fe Ox is a big driver of concentrations
## 
## Call:
## lm(formula = mg.C.per.g.soil_HF ~ FeOx, data = final_data %>% 
##     filter(Site == "LENO"))
## 
## Residuals:
##       1       2       3       4       5       6       7 
## -4.1075  4.7009 -1.6658  2.2706 -1.6290  0.2162  0.2147 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)   
## (Intercept)    3.594      2.559   1.405  0.21905   
## FeOx           9.070      2.148   4.222  0.00831 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 3.151 on 5 degrees of freedom
## Multiple R-squared:  0.781,  Adjusted R-squared:  0.7372 
## F-statistic: 17.83 on 1 and 5 DF,  p-value: 0.008308
summary(lm(mg.C.per.g.soil_HF~FeOx,data=final_data %>% filter(Site=="DELA") ))  #yes within this site Fe Ox is a big driver of concentrations
## 
## Call:
## lm(formula = mg.C.per.g.soil_HF ~ FeOx, data = final_data %>% 
##     filter(Site == "DELA"))
## 
## Residuals:
##        1        2        3        4        5        6 
## -1.73747 -0.98391  2.70828  2.51069  0.00396 -2.50155 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)  
## (Intercept)    1.900      3.359   0.566   0.6018  
## FeOx          12.713      3.577   3.555   0.0237 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.444 on 4 degrees of freedom
## Multiple R-squared:  0.7595, Adjusted R-squared:  0.6994 
## F-statistic: 12.63 on 1 and 4 DF,  p-value: 0.0237
summary(lm(FeOx~E,data=final_data )) #no overall trend with FeOx and ECM dominance across sites
## 
## Call:
## lm(formula = FeOx ~ E, data = final_data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -0.5086 -0.2592 -0.1037  0.1383  1.4144 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   0.4170     0.1120   3.725 0.000553 ***
## E             0.2107     0.1929   1.092 0.280790    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.4137 on 44 degrees of freedom
## Multiple R-squared:  0.02638,    Adjusted R-squared:  0.004257 
## F-statistic: 1.192 on 1 and 44 DF,  p-value: 0.2808
summary(lm(FeOx~E,data=final_data %>% filter(Site=="LENO") )) # yes within LENO positive relationship between soil FeOx and ECM dominance
## 
## Call:
## lm(formula = FeOx ~ E, data = final_data %>% filter(Site == "LENO"))
## 
## Residuals:
##        1        2        3        4        5        6        7 
##  0.64909 -0.03906 -0.09622 -0.24020 -0.15363  0.32898 -0.44897 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)  
## (Intercept)   0.3968     0.2776   1.429   0.2123  
## E             1.1924     0.4197   2.841   0.0362 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.4058 on 5 degrees of freedom
## Multiple R-squared:  0.6175, Adjusted R-squared:  0.541 
## F-statistic: 8.072 on 1 and 5 DF,  p-value: 0.0362
summary(lm(FeOx~E,data=final_data %>% filter(Site=="DELA") )) # not at all within DELA 
## 
## Call:
## lm(formula = FeOx ~ E, data = final_data %>% filter(Site == "DELA"))
## 
## Residuals:
##        1        2        3        4        5        6 
## -0.52932 -0.02020 -0.03696 -0.01102  0.24494  0.35256 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)  
## (Intercept)  0.88744    0.21678   4.094   0.0149 *
## E            0.02805    0.50443   0.056   0.9583  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.3415 on 4 degrees of freedom
## Multiple R-squared:  0.0007722,  Adjusted R-squared:  -0.249 
## F-statistic: 0.003091 on 1 and 4 DF,  p-value: 0.9583
#Site means of MAOM C and N concentrations and proportions, ordered by climate decomposition index

MAOM_C_conc_cdi=ggplot(final_data, aes(x=CDI, y=mg.C.per.g.soil_HF))+
   geom_point()+
  stat_summary(fun= mean, fun.min=mean, fun.max=mean, geom="crossbar", width=0.0009, position="dodge")+
  stat_summary(fun.data = mean_se, geom = "errorbar", width=0.0005, position = position_dodge(width = 0.0009))+
  geom_smooth(method="lm")+
                theme_cowplot()+
  xlab("Climate Decomposition Index")+
  ylab("[MAOM C] (mg/g soil)"
  )

MAOM_C_prop_cdi=ggplot(final_data, aes(x=CDI, y=proportion.C_HF))+
   geom_point()+
  stat_summary(fun= mean, fun.min=mean, fun.max=mean, geom="crossbar", width=0.0009, position="dodge")+
  stat_summary(fun.data = mean_se, geom = "errorbar", width=0.0005, position = position_dodge(width = 0.0009))+
  theme_cowplot()+
  xlab("Climate Decomposition Index")+
  ylab(expression("proportion C"[MAOM])
  )

MAOM_N_conc_cdi=ggplot(final_data, aes(x=CDI, y=mg.N.per.g.soil_HF))+
   geom_point()+
  stat_summary(fun= mean, fun.min=mean, fun.max=mean, geom="crossbar", width=0.0009, position="dodge")+
  stat_summary(fun.data = mean_se, geom = "errorbar", width=0.0005, position = position_dodge(width = 0.0009))+
  theme_cowplot()+
  xlab("Climate Decomposition Index")+
  ylab("[MAOM N] (mg/g soil)"
  )

MAOM_N_prop_cdi=ggplot(final_data, aes(x=CDI, y=proportion.N_HF))+
   geom_point()+
  stat_summary(fun= mean, fun.min=mean, fun.max=mean, geom="crossbar", width=0.0009, position="dodge")+
  stat_summary(fun.data = mean_se, geom = "errorbar", width=0.0005, position = position_dodge(width = 0.0009))+
  geom_smooth(method="lm")+
                theme_cowplot()+
  xlab("Climate Decomposition Index")+
  ylab(expression("proportion N"[MAOM])
  )

ggarrange(MAOM_C_conc_cdi, MAOM_C_prop_cdi, MAOM_N_conc_cdi, MAOM_N_prop_cdi, ncol=2, nrow=2, labels= c("a","b", "c", "d"), legend=F)
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'

ggsave("FigXXX.jpg", path="figures/", width= 24, height= 22, units="cm")
ggplot(final_data, aes(x=E, y=FeOx))+
  geom_point(color="blue")+
  geom_smooth(method="lm", color="blue")+
  facet_wrap(~Site)+
  theme_cowplot()
## `geom_smooth()` using formula 'y ~ x'

ggsave("ECM_vs_FeOx.jpg", path="figures/", width= 30, height= 24, units="cm")
## `geom_smooth()` using formula 'y ~ x'

`